Method and apparatus for providing information by using degree of association between reserved word and attribute language

ABSTRACT

Disclosed herein are a method and apparatus for providing information by using an attribute language. The method includes: extracting a representative attribute keyword candidate set including representative attribute keywords; setting a reserved word set including reserved words; storing the degrees of object-keyword association corresponding to object item-representative attribute keyword pairs; storing the degrees of basic reserved word-keyword association corresponding to reserved word-representative attribute keyword pairs by using association weights corresponding to representative attribute keyword-subordinate keyword pairs and the degrees of basic reserved word-subordinate keyword association corresponding to reserved word-subordinate keyword pairs; acquiring a received reserved word; acquiring the degree of reserved word-object association corresponding to a pair of the received reserved word and each object item by using the degrees of object-keyword association and the degrees of basic reserved word-keyword association; and providing an object item based on the degree of reserved word-object association.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority under 35 U.S.C. § 119 to Korean PatentApplication No. 10-2017-0099828 filed on Aug. 7, 2017, Korean PatentApplication No. 10-2017-0121289 filed on Sep. 20, 2017,PCT/KR2017/007964 filed on Jul. 24, 2017 and Korean Patent ApplicationNo. 10-2018-0033093 filed on Mar. 22, 2018, in the Korean IntellectualProperty Office (KIPO), the disclosure of which is incorporated byreference herein in its entirety.

1. TECHNICAL FIELD

At least some exemplary embodiments of the present disclosure relate toa method and apparatus for providing information by using the degree ofassociation between a reserved word and an attribute language.

2. DISCUSSION OF RELATED ART

According to conventional search methods, a user can search for adesired web document or the like by entering a search keyword into asearch box. For example, a user may retrieve information about the movie“Interstellar” by entering the title of the movie “Interstellar” intothe search box. However, if a user cannot remember the title of a moviewhich he or she desires to search for, he or she needs to provideanother type of information. For example, a user may attempt a search byentering an actor, director, producer, or the like of a movie which heor she desires to search for. There are many cases where movieinformation sites and movie review sites provide cast information aswell as movie information, and thus the user can search for a desiredmovie by using an actor, a director, a producer, or the like as akeyword unless he or she is unlucky.

Meanwhile, the conventional search methods cannot be used if informationto be used is information based on an a typical language, for example,an emotional language, rather than typical information. For example,responses provided by conventional search engines for a search term,such as “a funny movie” or “a movie which is viewed when a viewer issad,” are merely search results, including documents which have beenwritten to include the keyword “a funny movie” or “a movie viewed when aviewer is sad.” However, an a typical language requires an approachdifferent from that for typical information, such as a starring actor, a(typical) movie genre, and a year of release. Even if documents have notbeen written to include the keyword “a funny movie” or “a movie viewedwhen a viewer is sad,” there could be a lot of movies for which manypeople might feel is “fun” or “sad.” Furthermore, for other fields thanfilm, a different approach may be required for requesting information byusing an a typical language.

SUMMARY

At least some exemplary embodiments of the present disclosure aredirected to a method and apparatus for efficiently providing informationby using a reserved word.

According to an exemplary embodiment of the present disclosure, there isprovided a method of providing information, the method including:extracting a representative attribute keyword candidate set includingrepresentative attribute keywords; setting a reserved word set includingreserved words; storing the degrees of object-keyword associationcorresponding to object item-representative attribute keyword pairs;storing the degrees of basic reserved word-keyword associationcorresponding to reserved word-representative attribute keyword pairs byusing association weights corresponding to representative attributekeyword-subordinate keyword pairs and the degrees of basic reservedword-subordinate keyword association corresponding to reservedword-subordinate keyword pairs; acquiring a received reserved word;acquiring the degree of reserved word-object association correspondingto a pair of the received reserved word and each object item by usingthe degrees of object-keyword association and the degrees of basicreserved word-keyword association; and providing an object item based onthe degree of reserved word-object association corresponding to the pairof the received reserved word and each object item.

According to an exemplary embodiment of the present disclosure, there isprovided an apparatus for providing information, the apparatusincluding: a control unit configured to extract a representativeattribute keyword candidate set including representative attributekeywords, to set a reserved word set including reserved words, store thedegrees of object-keyword association corresponding to objectitem-representative attribute keyword pairs, and to store the degrees ofbasic reserved word-keyword association corresponding to reservedword-representative attribute keyword pairs by using association weightscorresponding to representative attribute keyword-subordinate keywordpairs and the degrees of basic reserved word-subordinate keywordassociation corresponding to reserved word-subordinate keyword pairs; astorage unit configured to store the degrees of object-keywordassociation and the degrees of basic reserved word-keyword association;and a communication unit configured to acquire a received reserved word.The control unit acquires the degree of reserved word-object associationcorresponding to a pair of the received reserved word and each objectitem by using the degrees of object-keyword association and the degreesof basic reserved word-keyword association. The control unit provides anobject item based on the degree of reserved word-object associationcorresponding to the pair of the received reserved word and each objectitem.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present invention will become moreapparent by describing in detail exemplary embodiments thereof withreference to the accompanying drawings, wherein:

FIG. 1 is a view showing the network configuration of a system forproviding information by using an attribute language according to anexemplary embodiment of the present disclosure;

FIG. 2 is a block diagram of a terminal according to an exemplaryembodiment of the present disclosure;

FIG. 3 is a block diagram of an information provision apparatusaccording to an exemplary embodiment of the present disclosure;

FIG. 4 is a flowchart of a process of providing information via aninformation provision interface according to an exemplary embodiment ofthe present disclosure;

FIG. 5 is a detailed flowchart of step 910 according to an exemplaryembodiment of the present disclosure;

FIG. 6 is a detailed flowchart of step 510 according to an exemplaryembodiment of the present disclosure;

FIG. 7 is a detailed flowchart of step 530 according to an exemplaryembodiment of the present disclosure;

FIG. 8 is a flowchart of a process of providing information according toanother exemplary embodiment of the present disclosure;

FIG. 9 is a flowchart of a process of providing information according toanother exemplary embodiment of the present disclosure;

FIG. 10 shows an example of the stored degrees of object-keywordassociation according to an exemplary embodiment of the presentdisclosure;

FIG. 11 shows an example of the degrees of basic reserved word-keywordassociation according to an exemplary embodiment of the presentdisclosure;

FIG. 12 is a detailed flowchart of step 940 according to an exemplaryembodiment of the present disclosure;

FIG. 13 is a flowchart of a process of providing information accordinganother exemplary embodiment of the present disclosure;

FIG. 14 is a flowchart of a process of providing information accordingto still another exemplary embodiment of the present disclosure;

FIG. 15 is a detailed flowchart of step 1340 according to a firstexemplary embodiment of the present disclosure;

FIG. 16 is a detailed flowchart of step 1340 according to anotherexemplary embodiment of the present disclosure;

FIG. 17 is a detailed flowchart of step 1340 according to still anotherexemplary embodiment of the present disclosure;

FIG. 18 is a detailed flowchart of step 1320 according to a modifiedexemplary embodiment of the present disclosure;

FIG. 19 is an example of an interface generated based on the interfaceinformation provided at step 1840; and

FIG. 20 is a view of a terminology hierarchy according to an exemplaryembodiment of the present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments of the present disclosure will be described indetail below with reference to the accompanying drawings.

In the descriptions of the embodiments, descriptions of techniques whichare well known in the art to which this disclosure belongs and which arenot directly related to this disclosure will be omitted. The reason forthis is to more clearly convey the gist of the present disclosurewithout making the gist of the present disclosure obscure by omittingunnecessary descriptions.

For the same reason, in the accompanying drawings, some components areexaggerated, omitted, or schematically shown. Also, the size of eachcomponent does not completely reflect the actual size thereof.Throughout the drawings, the same or corresponding components aredenoted by the same reference symbols.

The exemplary embodiments of the present disclosure are described indetail below with reference to the accompanying drawings.

FIG. 1 is a view showing the network configuration of a system forproviding information by using an attribute language according to anexemplary embodiment of the present disclosure.

Referring to FIG. 1, the information provision system according to thepresent exemplary embodiment may include a terminal 200, an informationprovision apparatus 300, and a communication network 150.

Terminal 200 may be implemented as, e.g., a smartphone, a PDA, a tabletPC, a notebook computer, a laptop computer, a personal computer, anotherelectronic device capable of performing communication, receiving inputfrom a user, and outputting screens, or a similar device.

The information provision apparatus 300 may be implemented as, e.g., aworkstation, a server, a general-purpose computer, another electronicdevice capable of performing communication, or a similar device.

The terminal 200 and the information provision apparatus 300 areconnected to and communicate with each other over the communicationnetwork 150.

The communication network 150 may be implemented using at least part ofLong Term Evolution (LTE), LTE-Advanced (LTE-A), WI-FI, Local AreaNetwork (LAN), Wide Area Network (WAN), Code Division Multiple Access(CDMA), Time Division Multiple Access (TDMA), Wireless Broadband(WiBro), and Global System for Mobile Communications (GSM), and othercommunication methods developed in the past, being currently developed,and to be developed in the future. In the following, for the sake ofconvenience, the terminal 200 and the information provision apparatus300 will be described as directly communicating with each other withoutreferences to the communication network 150.

The detailed operations and configurations of the terminal 200 and theinformation provision apparatus 300 will be described with reference toFIGS. 2 to 8.

FIG. 2 is a block diagram of a terminal 200 according to an exemplaryembodiment of the present disclosure.

Referring to FIG. 2, the terminal 200 according to the present exemplaryembodiment may include an input unit 210, a display unit 220, acommunication unit 230, a storage unit 240, and a control unit 250.

The input unit 210 converts an input operation of a user into an inputsignal, and transmits the input signal to the control unit 250. Theinput unit 210 may be implemented as, e.g., a keyboard, a mouse, a touchsensor on a touch screen, a touchpad, a keypad, a voice input device, oranother input processing device developed in the past, being currentlydeveloped, or to be developed in the future. For example, the input unit210 may receive information provision request input from a user, and maytransfer the information provision request input to the control unit250.

The display unit 220 outputs a screen under the control of the controlunit 250. The display unit 220 may be implemented as, e.g., a liquidcrystal display (LCD) device, a light-emitting diode (LED) device, anorganic LED (OLED) device, a projector, or another display devicedeveloped in the past, being currently developed, or to be developed inthe future. For example, the display unit 220 may display an interfacepage or information provision result page for the provision ofinformation. In an exemplary embodiment, a component using anothermethod capable of transferring information to a user, such as voiceoutput or vibration, rather than screen output, may be used in place ofthe display unit 220.

The communication unit 230 exchanges data with the information provisionapparatus 300 and/or other external devices. The communication unit 230transfers data, received from the information provision apparatus 300,to the control unit 250. Furthermore, the communication unit 230transfers data to the information provision apparatus 300 under thecontrol of the control unit 250. The communication technology used bythe communication unit 230 may vary depending on the type ofcommunication network 150 or other circumstances.

The storage unit 240 stores data under the control of the control unit250, and transfers requested data to the control unit 250.

The control unit 250 controls the overall operation of the terminal 200and individual components. In particular, the control unit 250 transmitsan information provision request or another type of data to theinformation provision apparatus 300 according to information input fromthe input unit 210, and displays a result page and/or an interface pagevia the display unit 220 according to page information received from theinformation provision apparatus 300, as will be described later.

The operation performed by the control unit 250 may be distributed andprocessed by a plurality of arithmetic and logic units which arephysically distributed. There is possible a method in which part of theoperation performed by the control unit 250 is performed by a firstserver and the remaining operation is performed by a second server. Inthis case, the control unit 250 may be implemented as the sum of thearithmetic and logic units which are physically distributed.

The storage unit 240 may be implemented as the sum of storage deviceswhich are physically separated from each other.

When the control unit 250 or storage unit 240 is implemented as the sumof a plurality of devices which are physically separated from eachother, communication is required between the plurality of devices. Inthis case, for the sake of simplicity of description, the followingdescription will be given on the assumption that the storage unit 240 orcontrol unit 250 is implemented as a single object.

In the case where the terminal 200 transmits or receives data, thecommunication unit 230 may be described as transmitting or receivingdata under the control of the control unit 250, or the control unit 250may be described as transmitting or receiving data by controlling thecommunication unit 230, depending on the point of view of acorresponding situation.

The detailed operations of the individual components of the terminal 200will be described with reference to FIGS. 4 to 8.

FIG. 3 is a block diagram of an information provision apparatus 300according to an exemplary embodiment of the present disclosure.

Referring to FIG. 3, the information provision apparatus 300 accordingto the present exemplary embodiment may include a communication unit310, a control unit 320, and a storage unit 330.

The communication unit 310 exchanges data with the terminal 200 and/orother external devices. The communication unit 310 transfers data,received from the terminal 200, to the control unit 320. Furthermore,the communication unit 310 transfers data to the terminal 200 under thecontrol of the control unit 320. The communication technology used bythe communication unit 310 may vary depending on the type ofcommunication network 150 or other circumstances.

The storage unit 330 stores data under the control of the control unit320, and transfers data, requested by the control unit 320, to thecontrol unit 320.

The control unit 320 controls the overall operation of the informationprovision apparatus 300 and individual components. In particular, whenthe control unit 320 receives an interface page request, an informationprovision result page request, or another type of data via thecommunication unit 310, the control unit 320 retrieves required datafrom storage unit 330, generates load page information, and transferspage information to the terminal 200 via the communication unit 310, aswill be described later.

In the case where the information provision apparatus 300 transmits orreceives data, the communication unit 310 may be described astransmitting or receiving data under the control of the control unit320, or the control unit 320 may be described as transmitting orreceiving data by controlling the communication unit 310, depending onthe point of view of a corresponding situation.

The detailed operations of the individual components of the informationprovision apparatus 300 will be described with reference to FIGS. 4 to8.

According to another exemplary embodiment, data adapted to provideinformation by using a voice form or another method may be transmittedand received in place of a page adapted to visually provide information.

FIG. 4 is a flowchart of a process of providing information via aninformation provision interface according to an exemplary embodiment ofthe present disclosure.

At step 410, the control unit 320 of the information provision apparatus300 generates interface page information. The interface page isinformation required to generate an information interface page. Theinterface page is a page adapted to prompt the input of a user, toreceive the input of the user, and to transfer the input of the user tothe information provision apparatus 300. For example, the interface pageinformation may be in the form of an HTML document or another markuplanguage document. In another exemplary embodiment, the terminal 200 mayhave the form information of the interface page in advance, and only anitem corresponding to content may be transferred from the informationprovision apparatus 300 to the terminal 200. In the following, for thesake of convenience, the following description will be given on theassumption that the interface page information or another type of pageinformation is transferred in the form of an HTML document. However, thescope of the present disclosure is not limited thereto.

At step 420, the communication unit 310 of the information provisionapparatus 300 transfers the interface page information to the terminal200.

At step 430, the control unit 250 of the terminal 200 constructs aninterface page by using the interface page information. For example, thecontrol unit 250 may run a web browser, may interpret an HTML document,and may construct an interface page in the form of a web page. Aseparate application may be used in place of the web browser.

At step 440, the display unit 220 of the terminal 200 displays theinterface page to a user 400. The interface page may include aninterface in which, e.g., the user 400 may request the provision ofinformation, may input and/or select a keyword for the provision of theinformation, and may make other settings for the provision of theinformation.

At step 450, the input unit 210 of the terminal 200 receives theselection input of the user 400 via the input interface page, andtransfers the selection input to the control unit 250.

At step 460, the communication unit 230 of the terminal 200 transfersinput information adapted to identify the selection input of the user400 to the information provision apparatus 300 under the control of thecontrol unit 250.

At step 470, the control unit 320 of the information provision apparatus300 generates result page information by using the input (e.g., akeyword and/or another information provision setting) of the user 400. Apreparation process of generating the result page information and aprocess of generating the result page information will be described withreference to FIGS. 5 to 8 later. The result page information may beconstructed, e.g., in the form of an HTML document and/or in the form ofan image.

At step 480, the communication unit 310 of the information provisionapparatus 300 transfers the result page information to the terminal 200.

At step 490, the control unit 250 of the terminal 200 constructs aresult page by using the result page information received by thecommunication unit 230. For example, the control unit 250 may constructa result page by interpreting the result page information in an HTMLform.

At step 495, the display unit 220 of the terminal 200 provides theresult page to the user 400.

Although it is assumed that a page in a visual form is provided to theuser 400 in the exemplary embodiment of FIG. 4, the interface or resultinformation may be provided by voice. In this case, a voice output unitmay be used in place of the display unit 220. Another type of interfacemethod available currently or in the future may be used in conjunctionwith the user 400 in place of the visual/aural method. In this case, theinformation provision apparatus 300 may provide information, obtainedthrough conversion using another method, to the terminal 200 in place ofthe page information in accordance with the interface method.

In exemplary embodiments shown in the drawings starting from FIG. 5, theuser 400 desires to receive information about an object in a specificfield of interest in which he or she is interested in. However, thescope of the present disclosure is not limited thereto.

A field of interest may be, e.g., the type of objects. For example, whenthe field of interest is “Celebrity,” objects corresponding to thisfield of interest may include “Si-min Yu,” “Jae-seok Yu,” “TaylorSwift,” etc. For example, when the field of interest is “Movie,” objectscorresponding to this field of interest may include “Dunkirk,”“Spider-Man: Homecoming,” “Despicable Me 3,” etc. For example, when thefield of interest is “Broadcast program,” objects corresponding to thisfield of interest may include “Muhandogeon (Infinite Challenge),”“American Idol,” “Game of Thrones,” etc.

In the following exemplary embodiments, documents are collected in orderto evaluate the relationship (the degree of association, weight, and/orthe like) between keywords. The collected documents may be evaluated ashaving the same value, or a newer document may be evaluated as having ahigher value. In other words, the degrees of association between the ageof a document based on an evaluation date and keywords appearing in thedocument may have a negative correlation.

In the process starting from FIG. 5, the value may vary depending on theup-to-dateness of a document. For example, the degree of association ofa case where two keywords appear in a document which is one day old atevaluation time may be evaluated as being ten times higher than that ofa case where two keywords appear in a document which is ten days old atthe evaluation time. The age of a document may be evaluated, e.g., on asecond/minute/hour basis or on a day/month/year basis. Although thecontrol unit 320 is based on a document evaluated before the age of thedocument is reflected therein, the control unit 320 may extract thedegree of association between two keywords by extracting the partialdegree of association reflecting the age of the document through thedivision of the value of the partial degree of association by the age ofthe document and then accumulating the partial degrees of association.

The time at which a document was generated, which is used to determinethe age of the document, may be determined using, e.g., a posting timeincluded inside the document and/or metadata. Alternatively, when adocument which had not been found during previous crawling is newlyfound through periodic crawling, it is determined that a new document isadded at new crawling time.

FIG. 9 is a flowchart of a process of providing information according toan exemplary embodiment of the present disclosure.

At step 910, the control unit 320 stores the degree of object-keywordassociation corresponding to each object item-representative attributekeyword pair in the storage unit 330.

FIG. 10 shows an example of the stored degrees of object-keywordassociation according to an exemplary embodiment of the presentdisclosure.

In the exemplary embodiment of FIG. 10, object items are all in m (i₁ toi_(m)) in number, and representative attribute keywords are all n (k₁ tok_(n)) in number.

For example, the degree of object-keyword association between the objectitem i₅ and the representative attribute keyword k₃ is w_(5,3).

The process of step 910 may be performed, e.g., according to part of theexemplary embodiments of FIGS. 5 to 8, a similar process, or anequivalent process. According to another exemplary embodiment, theprocess of step 910 may be performed by the input of an administrator,or by receiving the degree of object-keyword association, determined byan external system, via a network or storage medium.

FIG. 5 is a detailed flowchart of step 910 according to an exemplaryembodiment of the present disclosure.

Referring to FIG. 5, at step 510, the control unit 320 extracts arepresentative attribute keyword candidate set from first set documents.For example, the control unit 320 may collect keywords, frequentlyappearing in the documents of the first set documents corresponding to afield of interest, as a representative attribute keyword candidate set.

FIG. 6 is a detailed flowchart of step 510 according to an exemplaryembodiment of the present disclosure.

The control unit 320 may select keywords appearing in the same documentsas object keywords representative of object items belonging to aspecific field and keywords appearing in the same documents as fieldkeywords representative of a specific field as a first attribute keywordcandidate set and a second attribute keyword candidate set.

For example, when a target field of interest for the provision ofinformation provision service is “Celebrity,” field keywords may include“celebrity,” “entertainer,” “movie star,” “star,” “celeb,” etc. Thefield keywords may be set by an administrator, and may be recommendedand set by the control unit 320. The control unit 320 may acquire somefield keywords, and may then recommend and set similar keywords, whosedegree of association with each of the field keywords is analyzed asbeing equal to or larger than a preset value, as additional fieldkeywords.

When a target field of interest for the provision of informationprovision service is “Celebrity,” object keywords may be individualpersons belonging to the corresponding field of interest. For example“Jae-seok Yu,” “Taylor Swift,” “Stephen Curry,” etc. may be objectkeywords corresponding to the field of interest “Celebrity.”

The relationship between a field keyword and an object keyword is nowdescribed. For example, a field keyword may correspond to the attributeor type of corresponding object keyword. A field keyword may berepresentative of a set, whereas an object keyword may be representativeof an element belonging to a corresponding set.

Object keywords may be set by an administrator, and may be selectedusing a method similar to the method of selecting field keywords.According to still another exemplary embodiment, the control unit 320may select keywords, determined to be elements of a set represented by afield keyword, as object keywords by analyzing the contexts of collecteddocuments.

A popular object keyword and an unpopular object keyword may bedistinguished from each other based on the quantities of thefound/collected corresponding object keywords. The control unit 320 maysearch for/collect documents containing each object keyword, and may setan object keyword, for which the quantity of collected documents isequal to or larger than a specific threshold value, as a popular objectkeyword and set an object keyword, for which the quantity of collecteddocuments is smaller than a specific threshold value, as an unpopularobject keyword.

A popular field keyword and an unpopular field keyword may bedistinguished from each other based on the quantities of thefound/collected corresponding field keywords. The control unit 320 maysearch for/collect documents containing each field keyword, and may seta field keyword, for which the quantity of collected documents is equalto or larger than a specific threshold value, as a popular field keywordand set a field keyword, for which the quantity of collected documentsis smaller than a specific threshold value, as an unpopular fieldkeyword. However, the threshold value used to distinguish the popularobject keyword and the unpopular object keyword from each other and thethreshold value used to distinguish the popular field keyword and theunpopular field keyword from each other may be different values. In thefollowing, for the sake of convenience, a popular object keyword and apopular field keyword may be collectively called a popular field/objectkeyword. Furthermore, for the sake of convenience, an unpopular objectkeyword and an unpopular field keyword may be collectively called anunpopular field/object keyword.

In a modified exemplary embodiment, only a popular field keyword orpopular object keyword may be used in place of a popular field/objectkeyword. In a modified exemplary embodiment, only an unpopular fieldkeyword or unpopular object keyword may be used in place of an unpopularfield/object keyword.

At step 610, the control unit 320 sets keywords, appearing in the samedocuments as a popular field/object keyword, for a first attributekeyword candidate set.

The control unit 320 may search for/collect documents containing apopular field/object keyword, and may set keywords, included in thecollected documents, for a first attribute keyword candidate set.According to another exemplary embodiment, the control unit 320 mayexclude field keyword and object keywords among the keywords included inthe collected documents from the first attribute keyword candidate set.Furthermore, the control unit 320 may exclude a preset insignificantkeyword, e.g., a postpositional particle/article, from the firstattribute keyword candidate set. Furthermore, according to anotherexemplary embodiment, the control unit 320 may include a keyword,registered in a preset dictionary, among the keywords included in thecollected documents in a first attribute keyword candidate set.

Furthermore, according to another exemplary embodiment, the control unit320 may search for/collect documents containing a popular field/objectkeyword, and may include keywords, disposed within a preset distancefrom a popular field/object keyword or a sentence containing the keywordin the collected documents, in a first attribute keyword candidate set.Furthermore, according to another exemplary embodiment, the control unit320 may search for/collect documents containing a popular field/objectkeyword, and may include keywords, used to describe and modify thepopular field/object keyword, in a first attribute keyword candidate setby analyzing the contexts of the collected documents.

The distance between keywords or the distance between a keyword and asentence may be determined based on, e.g., any one or more of the numberof sentences located between the two keywords or between the keyword andthe sentence, the number of words located between the two keywords orbetween the keyword and the sentence, the number of phases locatedbetween the two keywords or between the keyword and the sentence, andthe number of letters located between the two keywords or between thekeyword and the sentence.

The control unit 320 may first perform morpheme analysis in order toperform keyword analysis.

At step 620, the control unit 320 sets keywords, appearing in the samedocuments as an unpopular field/object keyword, for a second attributekeyword candidate set.

The control unit 320 may search for/collect documents containing anunpopular field/object keyword, and may set keywords, included in thecollected documents, for a second attribute keyword candidate set.According to another exemplary embodiment, the control unit 320 mayexclude a field keyword and an object keyword among keywords included inthe collected documents from the second attribute keyword candidate set.Furthermore, the control unit 320 may exclude a preset insignificantkeyword, e.g., a postpositional particle/article and/or the like, fromthe second attribute keyword candidate set. Furthermore, according toanother exemplary embodiment, the control unit 320 may include akeyword, registered in a preset dictionary, among the keywords includedin the collected documents in a second attribute keyword candidate set.

Furthermore, according to another exemplary embodiment, the control unit320 may search for/collect documents containing an unpopularfield/object keyword, and may include keywords, disposed within a presetdistance from an unpopular field/object keyword or a sentence containingthe keyword in the collected documents, in a second attribute keywordcandidate set. Furthermore, according to another exemplary embodiment,the control unit 320 may search for/collect documents containing anunpopular field/object keyword, and may include keywords, used todescribe and modify the unpopular field/object keyword, in a secondattribute keyword candidate set by analyzing the contexts of thecollected documents.

The distance between keywords or the distance between a keyword and asentence may be determined based on, e.g., any one or more of the numberof sentences located between the two keywords or between the keyword andthe sentence, the number of words located between the two keywords orbetween the keyword and the sentence, the number of phases locatedbetween the two keywords or between the keyword and the sentence, andthe number of letters located between the two keywords or between thekeyword and the sentence.

The control unit 320 may first perform morpheme analysis in order toperform keyword analysis.

At step 630, the control unit 320 may set keywords belonging to both thefirst attribute keyword candidate set and the second attribute keywordcandidate set for a representative attribute keyword candidate set. Inother words, keywords used to modify both a popular field/object keywordand an unpopular field/object keyword may be collected as therepresentative attribute keyword candidate set.

According to another exemplary embodiment, at step 510, the control unit320 may include keywords each appearing along with an object keywordand/or a field keyword in the representative attribute keyword candidateset regardless of the popularity/unpopularity thereof.

Referring back to FIG. 5, at step 520, the control unit 320 extracts twoor more subordinate keywords, associated with each representativeattribute keyword included in the representative attribute keywordcandidate set, from the second set documents.

The second set documents used for the subordinate keyword extraction ofstep 520 and the first set documents used for the representativeattribute keyword candidate set extraction of step 510 may be differentdocument sets, or may be the same document set. For example, the firstset documents may be a set including all collectable documents, and thesecond set documents may be a set including only documents in which aspecific target field of interest for the provision of informationprovision service is used as a main keyword. The control unit 320 mayanalyzes whether or not each document is a document in which a specifictarget field of interest for the provision of information provisionservice is used as a main keyword based on frequently appearing keywordsby analyzing collectable documents. According to another exemplaryembodiment, the first set documents and the second set documents may beall sets each including all collectable related documents. Furthermore,according to another exemplary embodiment, the first set documents maybe a set including all collectable related documents, and the second setdocuments may be a set including only documents related to a specifictarget field of interest for the provision of information provisionservice. Furthermore, according to another exemplary embodiment, thesecond set documents may be a set including all collectable relateddocuments, and the first set documents may be a set including onlydocuments related to a specific target field of interest for theprovision of information provision service.

For step 520, the control unit 320 may collect documents including akeyword representative of a specific target field of interest itselfand/or documents each including an object keyword belonging to thecorresponding field of interest, e.g., in order to generate a setincluding only documents related to the specific field of interest forthe provision of information provision service, extracts documents inwhich the weight of a field keyword/object keyword is equal to or largerthan a preset value, from among the collected documents, and maygenerate a set including only documents related to the specific field ofinterest. The weight of the field keyword/object keyword may bedetermined based on the appearing frequency or appearing locations ofthe field keyword/object keyword, context, or the like. For example, adocument in which the field keyword/object keyword appears frequently,is used as the title of the corresponding document, or is described inlarge letters or emphasizing fonts may be classified as a documentrelated to the specific field of interest.

At step 520, the control unit 320 may extract a preset number ofsubordinate keywords each having a high degree of association with eachrepresentative attribute keyword by, e.g., analyzing at least part ofthe second set documents, thereby extracting two or more subordinatekeywords associated with each representative attribute keyword.

The control unit 320 may determine the degree of association between arepresentative attribute keyword and a subordinate keyword, e.g., bytaking into account the frequency at which the subordinate keywordappears in the same or similar context as the representative attributekeyword. For example, words appearing near keyword A in a specificsentence may be viewed as also appearing near a word associated withkeyword A in another document.

“I went on a trip after making a hard decision, but it was July and,thus, the weather was so hot that I suffered.”

“I went on a trip after making a hard decision, but it was July and,thus, the weather was so humid that I suffered.”

Referring to the above two sentences, the word “hot” is replaced withthe word “humid” in the same context. The control unit 320 may inferthat “hot” and “humid” are associated words.

“I went on a trip after making a hard decision, but it was July and,thus, the weather was so hot that I suffered.”

“I went on vacation after making a hard decision, but it was July and,thus, the weather was so hot that I suffered.”

In the same manner, the control unit 320 may infer from the above twosentences that “trip” and “vacation” are associated words.

“I went on a trip after making a hard decision, but it was July and,thus, the weather was so hot that I suffered.”

“I went on a trip after making a hard decision, but it was August and,thus, the weather was so hot that I suffered.”

In the same manner, the control unit 320 may infer that “July” and“August” are associated words.

The control unit 320 may stores information in which “hot” and “humid”are associated words, “July” and “August” are associated words, and“trip” and “vacation” are associated words via previously collecteddocuments. Thereafter, it is assumed that the following sentences arecollected.

“I went on vacation after making a hard decision, but it was July and,thus, the weather was so hot that I suffered.”

“I went on a trip after making a hard decision, but it was August and,thus, the weather was so hot that I went through hardship.”

When the two sentences do not have the same context but it is known that“hot” and “humid” are associated words, “July” and “August” areassociated words, and “trip” and “vacation” are associated words, thecontrol unit 320 may learn that “suffer” and “hardship” are alsoassociated words via the above sentences.

It may be determined that a keyword pair having a high appearingfrequency in the same/similar contexts has a high degree of association.Furthermore, it is determined that the higher the similarity betweencontexts in which two keywords appear is, the higher the degree ofassociation between the two keywords is. The control unit 320 mayincrease the accuracy of the determination of the degrees of associationbetween keywords in such a manner as to set the degrees of associationkeywords by performing learning by using collected documents and thensetting the degrees of association between keywords appearing in acorresponding sentence by using the set degrees of association betweenkeywords and the context of the sentence.

As similar learning methods, Neural Net Language Model (NNLM), RecurrentNeural Net Language Model (RNNLM), word2vec, skipgram, and ContinuousBag-of-Words (CBOW) methods are known. In particular, when the word2vecmethod is used, the word2vec method can map individual keywords tovectors by performing learning by using documents, and can determine thesimilarity between two keywords through the cosine similaritycalculation of two vectors.

By means of such a method or a similar method, the control unit 320 mayextract a preset number of subordinate keywords having the highestdegree of association with each representative attribute keyword byanalyzing at least part of the second set documents.

At step 530, the control unit 320 may extract an association weightcorresponding to a pair of each representative attribute keyword withinthe representative attribute keyword candidate set and each subordinatekeyword from the second set documents.

FIG. 7 is a detailed flowchart of step 530 according to an exemplaryembodiment of the present disclosure.

At step 710, the control unit 320 may extract the degrees of associationbetween the subordinate keywords by analyzing at least part of thesecond set documents. For example, it is assumed that subordinatekeywords collected as subordinate keywords associated withrepresentative attribute keyword A1 are 50 subordinate keywords B1₁ toB1₅₀. In this case, the control unit 320 may extract the degree ofassociation between two subordinate keywords by using the frequency atwhich the two subordinate keywords appear in the same document, forthese 50 subordinate keywords. The degree of association between B1₁ andB1₂ is determined based on the frequency at which B1₁ and B1₂ appear inthe same document. According to another exemplary embodiment, thefrequency at which B1₁ and B1₂ appear in the same document influencesthe degree of association, and, additionally, in the case where B1₁ andB1₂ appear in the same document, as the distance between the twokeywords B1₁ and B1₂ (or the distance between the sentences in which twokeyword appear) is closer, a higher degree of association may berecognized. In a similar method, the degrees of association betweensubordinate keywords may be extracted. The distance between keywords orthe distance between a keyword and a sentence may be determined basedon, e.g., any one or more of the number of sentences located between thetwo keywords or between the keyword and the sentence, the number ofwords located between the two keywords or between the keyword and thesentence, the number of phases located between the two keywords orbetween the keyword and the sentence, and the number of letters locatedbetween the two keywords or between the keyword and the sentence.

At step 720, the control unit 320 may extract association weightsbetween each representative attribute keyword and the subordinatekeywords based on the degrees of association between the subordinatekeywords. For example, for a subordinate keyword set corresponding toeach representative attribute keyword, the control unit 320 may set aspecific subordinate keyword within the subordinate keyword set and therepresentative attribute keyword so that the degree of associationbetween the specific subordinate keyword within the subordinate keywordset and another subordinate keyword within the subordinate keyword setand an association weight between the specific subordinate keyword andthe representative attribute keyword have a positive correlationtherebetween.

For example, the higher the degrees of association between thesubordinate keyword B1₁ of the representative attribute keyword A1 andother subordinate keywords B1₂ to B1₅₀ of the representative attributekeyword A1 are, the higher value the association weight between A1 andB1₁ may be set to. For example, the arithmetic mean (or sum) of thedegrees of association between B1₁ and the other subordinate keywordsB1₂ to B1₅₀ of A1 may become the association weight between B1₁ and A1.A geometric mean/harmonic mean may be used in place of a simplearithmetic mean. There may be used a truncated mean designed tocalculate a mean with the two highest ones (examples) of the degrees ofassociation between B1₁ and the other subordinate keywords B1₂ to B1₅₀of A1 and the two lowest ones (examples) thereof excluded from thecalculation. A median may be used in place of the arithmetic mean of thedegrees of association.

According to some exemplary embodiments, “the frequency at which B1₁ andB1₂ appear in the same document” used to calculate the associationweight of B1₁ for A1 does not vary simply depending on the number ofdocuments in which B1₁ and B1₂ appear together (in which B1₁ and B1₂appear in the same sentence, or in which B1₁ and B1₂ appear in closeproximity to each other), but may be obtained by dividing the number ofdocuments in which B1₁ and B1₂ appear together (in which B1₁ and B1₂appear in the same sentence, or in which B1₁ and B1₂ appear in closeproximity to each other) by the number of documents in which B1₁ appearsand/or the number of documents in which B1₂ appears. In a similarmanner, “the frequency at which B1₁ and B1₂ appear in the same document”may be set such that it has a positive correlation in connection withthe number of documents in which B1₁ and B1₂ appear together (in whichB1₁ and B1₂ appear in the same sentence, or in which B1₁ and B1₂ appearin close proximity to each other) and has a negative correlation inconnection with the number of documents in which B1₁ appears and/or thenumber of documents in which B1₂ appears. This is a kind ofnormalization intended to prevent a frequently used word from simplyhaving a high association weight in connection with the representativeattribute keyword A1.

Referring back to FIG. 5, at step 540, the control unit 320 may extractthe degrees of subordinate association between an object item andsubordinate keywords from the first set documents.

It may be determined that subordinate keywords frequently appearing inthe same document, the same sentence or a close sentence as an objectkeyword (for example “Taylor Swift”) representative of an object item inthe first set documents are associated with the corresponding objectitem. The control unit 320 may collect documents in which the objectkeyword of the corresponding object item appears, and may extract thedegree of subordinate association between each subordinate keyword andthe object keyword based on the frequency at which they appear togetherwithin the documents. In particular, when a subordinate keyword appearsin the same sentence as the object keyword, the control unit 320 may setthe degree of association between the subordinate keyword and the objectitem to a higher value than when the subordinate keyword appears in asentence different from that in which the object keyword appears.

The control unit 320 may set the degree of association between thesubordinate keyword and the object item of the corresponding objectkeyword to a higher value in proportion to the proximity between asentence in which the subordinate keyword appears and a sentence inwhich the object keyword appears. The proximity between two sentencesmay be determined based on, e.g., any one or more of the number ofsentences located between the two sentences, the number of words locatedbetween the two sentences, the number of phases located between the twosentences, and the number of letters located between the two sentences.

The control unit 320 may set the degree of association between thesubordinate keyword and the object item of the corresponding objectkeyword to a higher value in proportion to the proximity between alocation at which the subordinate keyword appears and a location atwhich the object keyword appears. The proximity between the subordinatekeyword and the object keyword may be determined based on, e.g., any oneor more of the number of sentences located between the subordinatekeyword and the object keyword, the number of words located between thesubordinate keyword and the object keyword, the number of phases locatedbetween the subordinate keyword and the object keyword, and the numberof letters located between the subordinate keyword and the objectkeyword.

At step 550, the control unit 320 may extract the degree ofobject-keyword association between the object item and therepresentative attribute keyword by using the degrees of subordinateassociation of step 540 and the association weights of step 530.

For example, the degree of object-keyword association between objectitem C and the representative attribute keyword A1 may be extractedusing the degrees of subordinate association between C and thesubordinate keywords (e.g., B1₁ to B1₅₀) of A1 and the associationweights of the respectively subordinate keywords. For example, thedegree of object-keyword association between the object item C and therepresentative attribute keyword A1 may be set to a higher value inproportion to the degrees of subordinate association between the objectitem C and the subordinate keywords B1₁ to B1₅₀.

When the degree of subordinate association with the object item C ishigher for a subordinate keyword having a higher association weight inthe relationship with A1, the degree of object-keyword associationbetween the object C and the representative attribute keyword A1 may beset to a higher value for a subordinate keyword having a lowerassociation weight than a case having a higher degree of subordinateassociation. For example, the degree of subordinate association of akeyword B1₁ having a higher association weight is higher in table 1 thanin table 2, and thus the degree of object-keyword association betweenthe object C and the representative attribute keyword A1 may be set to ahigher value in table 1 than in table 2.

TABLE 1 Association weight in Degree of subordinate connection with A1association with C B1₁ 0.5 0.5 B1₂ 0.2 0.2

TABLE 2 Association weight in Degree of subordinate connection with A1association with C B1₁ 0.2 0.5 B1₂ 0.5 0.2

According to an exemplary embodiment, the degree of object-keywordassociation between the object C and the representative attributekeyword A1 may be obtained based on (or using) the sum of valuesobtained by multiplying association weights and the degrees ofsubordinate association corresponding to the individual subordinatekeywords. In table 1, 0.5×0.5+0.2×0.2=0.29, and in table 2,0.2×0.5+0.5×0.2=0.20. Accordingly, the degree of object-keywordassociation between the object C and the representative attributekeyword A1 may be set to a higher value in table 1 than in table 2.

The above-described method of calculating the degree of object-keywordassociation is merely an example. As long as the degree of subordinateassociation in connection with C obtained at step 540 and theassociation weight in connection with A1 obtained at step 530 have apositive correlation with the degree of object-keyword associationbetween C and A1, another method may be used.

Thereafter, when the communication unit 310 receives a request for theprovision of information associated with the specific representativeattribute keyword, the control unit 320 may provide a result item viathe communication unit 310 based on the degree of object-keywordassociation extracted at step 550. For example, when receiving a requestfor the provision of information including any one representativeattribute keyword, the control unit 320 may provide information aboutobject items in descending order of the degree of object-keywordassociation in the relationship with the corresponding representativeattribute keyword.

In another exemplary embodiment, when receiving a request for theprovision of information including two or more representative attributekeywords and corresponding weights, the control unit 320 may provideinformation about object items in descending order of the sum (or mean)of values obtained by multiplying the degrees of object-keywordassociation with the representative attribute keywords included in therequest for the provision of information by weights (or adding weightsto the degrees of object-keyword association) for each object item.

FIG. 8 is a flowchart of a process of providing information according toanother exemplary embodiment of the present disclosure.

The exemplary embodiment of FIG. 8 further includes two steps 523 and526 between steps 520 and 530 in addition to processes identical tothose of the exemplary embodiment of FIG. 5. In this case, redundantdescriptions will be omitted, and only steps 523 and 526 will bedescribed.

At step 523, the control unit 320 determines whether each of thesubordinate keywords extracted at step 520 corresponds to an emotionalword. For this purpose, the storage unit 330 or external server may holdan emotional word dictionary. The emotional word dictionary is a toolfor determining whether or not a word (keyword) is an emotional word,and may hold, e.g., an emotional word list. It may be determined that akeyword included in the emotional word list is an emotional word and akeyword not included in the emotional word list is not an emotionalword. However, these determinations are based on dictionary meanings,and may not reflect the use of words by the public, which varies overtime. Accordingly, the control unit 320 determines whether to use arepresentative attribute keyword based on whether or not subordinatekeywords associated with the representative attribute keyword areemotional words without determining whether or not the representativeattribute keyword itself is an emotional word.

In another exemplary embodiment, the control unit 320 may add anotherword, having a high degree of association (equal to or larger than apreset value) with a preset or larger number of words registered in theemotional word dictionary as emotional words, to the emotional worddictionary.

At step 526, the control unit 320 may leave a preset number ofrepresentative attribute keywords in a representative attribute keywordcandidate set in descending order of the emotional word percentage (ornumber) of associated subordinate keywords, and may eliminate theremainder. Through this process, a keyword distant from an emotionalword may be prevented from being treated as an emotional word.

Referring back to FIG. 9, at step 920, the control unit 320 stores thedegree of basic reserved word-keyword association corresponding to eachreserved word-representative attribute keyword pair in the storage unit330.

Reserved words may include expressions which can be presented by theweights of representative attribute keywords. For example, “boring” maybe a reserved word, and “pretty” may be a reserved word.

Representative attribute keywords each having a high degree of basicreserved word-keyword association in connection with the reserved word“boring” may include representative attribute keywords related to theresolution of a boring situation, such as “interesting,” “exciting,”“time-killing,” etc.

Representative attribute keywords having a high degree of basic reservedword-keyword association in connection with the reserved word “pretty”may include representative attribute keywords similar to “pretty” anddescribing “pretty,” such as “beautiful,” “cute,” “attractive,” etc.

For example, the process of step 920 may be performed by input of anadministrator, or by receiving the degree of basic reserved word-keywordassociation, determined by an external system, via a network or storagemedium. According to another exemplary embodiment, the process of step920 may be performed by analyzing collectable documents, such asInternet information, SNS information, news, etc., and using a methodsimilar to the processes of FIGS. 5 to 8. Furthermore, the process ofstep 920 may include a process of reflecting the feedback of a user, aswill be described later.

The process of step 920 may be performed using a method which will bedescribed later with reference to any one of FIGS. 15 to 17.

FIG. 11 shows an example of the degrees of basic reserved word-keywordassociation according to an exemplary embodiment of the presentdisclosure.

In the exemplary embodiment of FIG. 11, reserved words are all q (C₁ toC_(q)) in number, and representative attribute keywords are all n (k₁ tok_(n)) in number.

For example, the degree of basic reserved word-keyword associationbetween the reserved word C₅ and the representative attribute keyword k₃is v_(3,5).

At step 930, the communication unit 310 receives and acquires a receivedreserved word from the terminal 200, and transfers the received reservedword to the control unit 320.

A received reserved word is a reserved word received by the terminal 200from a search user. The terminal 200 may convert a voice input into anelectrical signal (a voice signal), and may transfer the voice signal tothe information provision apparatus 300. The control unit 320 of theinformation provision apparatus 300 may analyze the voice signal, mayconvert the voice signal into a text, and may match the text to areserved word. Furthermore, the control unit 320 may analyze theintonation, pitch, tempo, respiration state, etc. of a voice byanalyzing the voice signal, and may use analysis results as contextualinformation.

According to another exemplary embodiment, the terminal 200 may convertthe voice input into a text, and may transfer the text to theinformation provision apparatus 300. The terminal 200 may analyze theintonation, pitch, tempo, respiration state of a voice, etc., and maytransfer analysis information to the information provision apparatus300. The information provision apparatus 300 may use the analysisinformation as a type of contextual information.

At step 940, the control unit 320 may acquire the degree of reservedword-object association corresponding to a pair of the received reservedword and each object item by using the degree of object-keywordassociation and the degree of basic reserved word-keyword association.

FIG. 12 is a detailed flowchart of step 940 according to an exemplaryembodiment of the present disclosure.

Referring to FIG. 12, at step 1210, the control unit 320 acquires theadjusted degree of object-keyword association corresponding to a pair ofeach object item and the representative attribute keyword for thereceived reserved word.

According to an exemplary embodiment, for a pair of each object item anda representative attribute keyword, the control unit 320 may acquire theadjusted degree of object-keyword association corresponding to a pair ofeach object item and a representative attribute keyword for the receivedreserved word by applying the degree of basic reserved word-keywordassociation corresponding to a pair of the received reserved word andthe representative attribute keyword to the degree of object-keywordassociation corresponding to the pair of the object item and therepresentative attribute keyword.

In particular, for a pair of each object item and a representativeattribute keyword, the control unit 320 may acquire the adjusted degreeof object-keyword association corresponding to a pair of each objectitem and a representative attribute keyword for the received reservedword by using a value obtained by multiplying the degree ofobject-keyword association corresponding to the pair of the object itemand the representative attribute keyword by the degree of basic reservedword-keyword association corresponding to a pair of the receivedreserved word and the representative attribute keyword.

Furthermore, for a pair of each object item and a representativeattribute keyword, the control unit 320 may set the adjusted degree ofobject-keyword association corresponding to a pair of each object itemand a representative attribute keyword for the received reserved word sothat the adjusted degree of object-keyword association has a positivecorrelation with the degree of object-keyword association correspondingto the pair of the object item and the representative attribute keywordand has a positive correlation with the degree of basic reservedword-keyword association corresponding to a pair of the receivedreserved word and the representative attribute keyword.

In the present disclosure, it is assumed that the degree ofobject-keyword association, the degree of basic reserved word-keywordassociation, the adjusted degree of object-keyword association, thedegree of basic reserved word-subordinate keyword association, and othervalues each representative of the degree of association are values whichare each representative of a closer correlation in proportion to thesize of the value. In another exemplary embodiment, in the case wherethe value of a type of the degree of association is representative of acloser correlation in inverse proportion to the size of the value andthe value of another type of the degree of association value isrepresentative of a closer correlation in proportion to the size of thevalue, a positive correlation and a negative correlation areappropriately replaced with each other and then used in accordance withthe case.

For example, in order to acquire the adjusted degree of object-keywordassociation corresponding to a pair of object item i₄ and representativeattribute keyword k₃ when the received reserved word is C₂, the controlunit 320 may acquire the adjusted degree of object-keyword associationby applying the degree of basic reserved word-keyword associationv_(3,4) corresponding to a pair of the received reserved word C₂ and therepresentative attribute keyword k₃ to the degree of object associationw_(4,3) corresponding to the pair of the object item i₄ and therepresentative attribute keyword k₃.

In particular, a method of applying the degree of association may be amethod of multiplying the degree of object association and the degree ofbasic reserved word-keyword association. For example, in order toacquire the adjusted degree of object-keyword association correspondingto a pair of object item i₄ and representative attribute keyword k₃ whenthe received reserved word is C₂, the control unit 320 may acquire theadjusted degree of object-keyword association by using the value(w_(4,3)×v_(3,2)) obtained by multiplying the degree of objectassociation w_(4,3) corresponding to the pair of the object item i₄ andthe representative attribute keyword k₃ and the degree of basic reservedword-keyword association v_(3,2) corresponding to a pair of the receivedreserved word C₂ and the representative attribute keyword k₃. In anotherexemplary embodiment, the control unit 320 may acquire the adjusteddegree of object-keyword association by using function f(w_(4,3),v_(3,2)) based on another calculation/utilization method adapted toallow the adjusted degree of object-keyword association to have apositive correlation with w_(4,3) and v_(3,2) in place of themultiplication. Furthermore, both a method of using (w_(4,3)×v_(3,2)) asthe adjusted degree of object-keyword association and a method ofapplying another factor-based correction to (w_(4,3)×v_(3,2)) and thenusing a resulting value as the adjusted degree of object-keywordassociation may be used.

At step 1220, the control unit 320 may acquire the degree of reservedword-object association by using a value obtained by accumulating theadjusted degrees of object-keyword association for a specific objectitem. For example, the control unit 320 may set the degree of reservedword-object association corresponding to a pair of the received reservedword and the specific object item so that the degree of reservedword-object association has a positive correlation with the cumulativevalue of the adjusted degrees of object-keyword association for aspecific object item. The degree of reserved word-object associationcorresponding to a pair of object item i₄ and received reserved word C₂may be acquired using, e.g., Σ_(j=1) ^(n)f(w_(4,j),v_(j,2)).f(w_(4,j),v_(j,2)) is the adjusted degree of object-keyword associationcorresponding to object item i₄, received reserved word C₂, and keywordk_(j).

For example, the degree of reserved word-object associationcorresponding to a pair of object item i₄ and received reserved word C₂may be Σ_(j=1)^(n)(w_(4,j)×v_(j,2))=(w_(4,1)×v_(1,2))+(w_(4,2)×v_(2,2))+ . . .+(w_(4,n)×v_(n,2)). In another example, the degree of reservedword-object association corresponding to a pair of object item i₄ andreceived reserved word C₂ may be a value obtained by applying anotherfactor-based correction to Σ_(j=1) ^(n)(w_(4,j),v_(1,2)).

Referring back to FIG. 9, at step 950, the control unit 320 may providean object item according to the degree of reserved word-objectassociation corresponding to the received reserved word. For example,when the degrees of reserved word-object association corresponding toreceived reserved word C₂ are as shown in table 3, the control unit 320may provide object items in the order shown in table 4.

TABLE 3 Degree of reserved word-object Object item association withreceived reserved word i₁ 0.23 i₂ 0.33 i₃ 0.99 i₄ 0.84

TABLE 4 Degree of reserved word- object association with Order Objectitem received reserved word 1 i3 0.99 2 i4 0.84 3 i2 0.33 4 i1 0.23

In other words, the control unit 320 may provide object items indescending order of the degree of reserved word-object associationcorresponding to the received reserved word. The terminal 200 havingreceived the object items may provide information about object item i₃to a user via the display unit 220. The terminal 200 may provideinformation about another object item at a lower order position whennecessary. The terminal 200 may provide information about object item i3to a user by voice through a speaker in place of the display unit 220.

FIG. 13 is a flowchart of a process of providing information accordinganother exemplary embodiment of the present disclosure.

The processes of FIGS. 13 to 17 may be performed by using some of theprocesses of FIGS. 5 to 12 or modifying some of the processes of FIGS. 5to 12. When the processes of FIGS. 13 to 17 are described, descriptionsof FIGS. 5 to 12 may be quoted when necessary.

Referring to FIG. 13, at step 1310, the control unit 320 extracts arepresentative attribute keyword candidate set from first set documents.For example, the control unit 320 may collect keywords, frequentlyappearing in the documents of the first set documents corresponding to afield of interest, as a representative attribute keyword candidate set.The process of step 1310 may be performed in a manner identical orsimilar to, e.g., that of the process of step 510 of FIG. 5. The processof step 1310 may be performed in a manner identical and similar to thatof the process of FIG. 6. The description of the process of FIG. 6 isnot repeated.

At step 1320, the control unit 320 sets a reserved word set. Forexample, an administrator may set a reserved word set through manualinput. According to a modified exemplary embodiment, the control unit320 may set word phrases/passages, etc. suitable for reserved words asreserved word candidates, and may provide an interface configured toallow one or more of the reserved word candidates as reserved words.

FIG. 18 is a detailed flowchart of step 1320 according to a modifiedexemplary embodiment of the present disclosure.

At step 1810, the control unit 320 acquires the appearing frequency ofone linguistic unit or two or more consecutive linguistic units withinthe document set. In this case, the document set may be the same as ordifferent from the document set used in the process of step 510 of FIG.5.

The linguistic unit may be, e.g., any one of a word phrase, a word, amorpheme, a syllable, and a letter. Other units defined to segment asentence based on various criteria may be used as the linguistic unit inthe present exemplary embodiment.

Before step 1810, the control unit 320 may segment documents, includedin the individual documents of the document set, into word phrase unitsand store the word phrase units in the form of an array or list. In anexemplary embodiment, the control unit 320 may delete insignificantwords, e.g., some postpositional particles and demonstrative adjective,such as “

,” “

,” etc., in Korean, and other words not requiring analysis from eachword phrase, or may remove from the array or list. Furthermore, in anexemplary embodiment, when a word phrase is composed of one word, thecontrol unit 320 may convert the corresponding word into a basic orpreset form.

According to a modified exemplary embodiment, before step 1810, thecontrol unit 320 may segment documents, included in the individualdocuments of the document set, into word units and store the word unitsin the form of an array or list. In an exemplary embodiment, the controlunit 320 may convert each word into a basic (or preset) form. In anexemplary embodiment, the control unit 320 may remove insignificantwords, e.g., some postpositional particles and demonstrative adjective,such as “

,” “

,” etc., in Korean, and other words not requiring analysis from thearray or list.

There may be possible a modified exemplary embodiment in which thecontrol unit 320 segments documents into morpheme units, syllable units,or letter units.

In the following exemplary embodiment, for the sake of convenience, itis assumed that the control unit 320 segments the documents into wordphrase units and the word phrases become linguistic units.

A single linguistic unit may become a reserved word. According to amodified exemplary embodiment, two or more consecutive linguistic unitsalso become a reserved word. For example, “neat” (a single linguisticunit) may become a reserved word, “nicely atmospheric” (two consecutivelinguistic units) may become a reserved word. However, two or moreconsecutive linguistic units appear less frequently than a singlelinguistic unit. Accordingly, in an exemplary embodiment, a weight or anadditional score may be given to two or more consecutive linguisticunits upon the selection of reserved words so that the two or moreconsecutive linguistic units can be selected as a reserved word.According to a modified exemplary embodiment, upon the selection ofreserved words, a reference value for the selection of a reserved wordmay be set to a lenient value for two or more consecutive linguisticunit. For example, in the case where a single linguistic unit may berecommended as a reserved word candidate only when it appears at least“a” times, settings may be made such that two consecutive linguisticunits may be recommended as a reserved word candidate even when theyappear “b” times considerably less than “a” times and such that threeconsecutive linguistic units may be recommended as a reserved wordcandidate even when they appear “c” times less than “b” times. In thefollowing, two or more consecutive linguistic units are calledconsecutive linguistic units.

Furthermore, in the case where consecutive linguistic units arerecommended/selected as a reserved word, it is included in a linguisticunit included in the reserved word candidate or in the reserved wordcandidate itself, and consecutive linguistic units shorter than thereserved word candidate may be prevented from being recommended asreserved words, or a deduction in a score may be made during thecalculation of a score used to recommend the units as a reserved word.The reason for this is to prevent a plurality of similar reserved wordsfrom being selected or recommended. In the following, for the sake ofsimplicity of description, although descriptions of consecutivelinguistic units will be omitted, descriptions of a single linguisticunit may be applied to consecutive linguistic units in an identical orsimilar manner.

At step 1810, the appearing frequency of a linguistic unit may be, e.g.,the number of documents in which the corresponding linguistic unitappears. Even when a corresponding linguistic unit appears in a singledocument a plurality of times, only an appearing frequency of 1 isrecognized. In contrast, according to another exemplary embodiment, inthe case where a corresponding linguistic unit appears in a singledocument a plurality of times, the number of times may be all recognizedas an appearing frequency, and the appearing frequency may become theappearing frequency of the linguistic unit.

According to still another exemplary embodiment, in the case where acorresponding linguistic unit appears in a single document two or moretimes, second and later appearances may be evaluated as being lower thana first appearance. Furthermore, in the case where the appearance of acorresponding linguistic unit is repeated in a single document, laterappearances may be evaluated as being lower. Although a score increasesas the number of appearances increases, the gradient of scores graduallybecomes gentle. For example, an appearance frequency to the power of 1/r(where r is a real number larger than 1) may be used as the appearancescore of a corresponding linguistic unit in a corresponding document.For example, (the log value of an appearing frequency)+1 (where when theappearing frequency is 0, a corresponding appearance score is 0) or thelike may be used. Furthermore, the appearance score of a linguistic unitin a single document may be limited to a value equal to or smaller thana preset upper limit value. A value obtained by accumulating theappearance scores of a corresponding linguistic unit for all documentsmay become an appearance score based on the appearing frequencies of thecorresponding linguistic unit. Furthermore, this appearance score may beused at step 1830.

In the following, for the sake of convenience, the following descriptionwill be given on the assumption that the appearing frequency of alinguistic unit is the number of documents in which the correspondinglinguistic unit appears.

At step 1820, the control unit 320 acquires the frequency at which anemotional word is located within a preset distance from a linguisticunit. The distance between the linguistic unit and the emotional wordmay be determined based on, e.g., any one or more of the number of wordslocated between the linguistic unit and the emotional word, the numberof word phrases located between the linguistic unit and the emotionalword, and the number of letters located between the linguistic unit andthe emotional word.

Furthermore, when a linguistic unit and an emotional word belong todifferent sentences, the control unit 320 may determine that theemotional word is not located within the preset distance from thelinguistic unit regardless of the number of words, word phrases and/orletters located between the linguistic unit and the emotional word.According to another modified exemplary embodiment, when a linguisticunit and an emotional word belong to different sentences, the controlunit 320 may calculate the distance therebetween by adding apredetermined numerical value to a calculated distance without takinginto account the sentences. The reason for this is that when alinguistic unit and an emotional word belong to different sentences,probability that they have no correlation is stronger, and thus it ispreferable that the distance therebetween is evaluated as being longerthan the number of words, word phrases and/or letters located betweenthe linguistic unit and the emotional word.

Whether a specific word (a word phrase) is an emotional word may bedetermined by referring to the previously registered emotional worddictionary.

The frequency at which an emotional word is located within apredetermined distance from a linguistic unit may be, e.g., the numberof documents in which the corresponding linguistic unit and thecorresponding emotional word are located together within a predetermineddistance. Even when a corresponding linguistic unit and a correspondingemotional word appear within the predetermined distance a plurality oftimes in a single document, only an appearing frequency of 1 isrecognized. According to another exemplary embodiment, when an emotionalword is located within a predetermined distance from a plurality oflinguistic units, all cases where the emotional word is located withinthe predetermined distance from the individual linguistic units may berecognized as a frequency. In the following, a linguistic unit locatedwithin the predetermined distance from an emotional word is called anemotional word location linguistic unit.

According to still another exemplary embodiment, in the case where anemotional word location linguistic unit appears in a single document twoor more times, second and later appearances may be evaluated as beinglower than a first appearance. Furthermore, in the case where theappearance of a corresponding emotional word location linguistic unit isrepeated in a single document, later appearances may be evaluated asbeing lower. Although a score increases as the number of appearancesincreases, the gradient of scores gradually becomes gentle. For example,an appearance frequency to the power of 1/r (where r is a real numberlarger than 1) may be used as the appearance score of a correspondingemotional word location linguistic unit in a corresponding document. Forexample, (the log value of an appearing frequency)+l (where when theappearing frequency is 0, a corresponding appearance score is 0) or thelike may be used. Furthermore, the appearance score of an emotional wordlocation linguistic unit in a single document may be limited to a valueequal to or smaller than a preset upper limit value. A value obtained byaccumulating the appearance scores of a corresponding emotional wordlocation linguistic unit for all documents may become an appearancescore based on the appearing frequencies of the corresponding emotionalword location linguistic unit. Furthermore, this appearance score may beused at step 1830.

Furthermore, according to another exemplary embodiment, a higherappearance score may be recognized in proportion to the number ofemotional words located within a predetermined distance from onelinguistic unit. Furthermore, when an emotional word is located within ashorter distance from one linguistic unit, a higher appearance score maybe recognized. Furthermore, only when preset two or more emotional wordsare located within a predetermined distance from one linguistic unit mayan appearance score (an appearing frequency) be recognized.

For the sake of convenience, the following description will be given onthe assumption that the appearing frequency of an emotional wordlocation linguistic unit is the number of documents in which anemotional word appears within a preset distance from a correspondinglinguistic unit.

At step 1830, the control unit 320 selects a reserved word candidate bytaking into account the appearing frequency of a correspondinglinguistic unit and the frequency at which an emotional word is locatedwithin a predetermined distance from the linguistic unit.

For example, the control unit 320 obtains an emotional word score bymultiplying the appearing frequency of a corresponding linguistic unitand the frequency at which an emotional word is located within apredetermined distance from the linguistic unit (or by using acalculation method which has a positive correlation for the twovariables). Furthermore, a preset number of reserved word candidates maybe set in descending order of the score. Alternatively, linguistic unitscorresponding to a preset score or more may be set as reserved wordcandidates.

TABLE 5 Appearing Emotional word Linguistic unit frequency locationfrequency Score First linguistic unit 3003 1122 3369366 Secondlinguistic 2001 1820 3641820 unit Third linguistic unit 3121 13004057300 Fourth linguistic 200 110 22000 unit

In the example of table 5, the linguistic units may become reserved wordcandidates in order of the third linguistic unit->the second linguisticunit->the first linguistic unit->the fourth linguistic unit. When thecontrol unit 320 recommends two reserved word candidates, the thirdlinguistic unit and the second linguistic unit will be recommended. Whenthe control unit 320 recommends linguistic units equal to or larger thana perfect score of 300 as reserved word candidates, the third linguisticunit, the second linguistic unit, and the first linguistic unit will berecommended as reserved word candidates in order thereof.

According to another exemplary embodiment, the control unit 320 mayextract a first number of linguistic units in order of the appearingfrequencies of the linguistic units, and may then extract apredetermined number of reserved word candidates in descending orderbased on the frequency at which an emotional word appears within apredetermined distance from the extracted linguistic units (or anemotional word score). In the example of table 5, when three linguisticunits are extracted in descending order of their appearing frequency,the first to third linguistic units may be extracted. Based on theappearing frequencies of emotional words, reserved word candidates maybe recommended in order of the second linguistic unit, the thirdlinguistic unit, and the first linguistic unit.

Furthermore, according to another exemplary embodiment, the control unit320 may extract a first number of linguistic units in descending orderof the appearing frequencies of the linguistic units, and may thenextract a first number of reserved word candidate (where the secondnumber is less than the first number) in descending order based on thefrequency at which an emotional word appears within a predetermineddistance from the extracted linguistic units (or an emotional wordscore). In the example of table 5, when three linguistic units areextracted in descending order of their appearing frequency, the first tothird linguistic units may be extracted. When two linguistic units areextracted based on an emotional word appearing frequency, reserved wordcandidates may be recommended in order of the second linguistic unit andthe third linguistic unit.

Using another method similar or slightly different from theabove-described method, the control unit 320 may set a score for theselection of reserved word candidates so that the score for theselection of reserved word candidates has a positive correlation withthe appearing frequencies of linguistic units at step 1810 and also hasa positive correlation with the appearing frequencies of linguisticunits from which an emotional word is located within the preset distanceat step 1820, and may recommend reserved word candidates by using thescore.

Furthermore, the control unit 320 may perform processing such that alinguistic unit already included in a reserved word set can be preventedfrom being recommended as a reserved word candidate. Furthermore, thecontrol unit 320 may perform processing such that a linguistic unitsubstantially identical to a reserved word included in the reserved wordset can be prevented from being added to reserved word candidates.

At step 1840, the information provision apparatus 300 provides theterminal 200 with interface information adapted to generate a reservedword selection interface including reserved word candidate information.The interface information may be, e.g., a document in an html form.According to another exemplary embodiment, the interface information mayinclude only dynamic information (recommended reserved word candidates,etc.) required to generate the interface, and the terminal 200 mayprovide a page including the interface to a user in such a manner as toincorporate the dynamic information into a page form stored in theterminal 200 in advance.

The control unit 320 may generate page information adapted to generatethe page including the reserved word selection interface inclusive ofthe reserved word candidate information, and the communication unit 310may provide the page information to the terminal 200. The terminal 200may display the page including the corresponding interface to a userthrough rendering. According to a modified exemplary embodiment, aninterface based on sound or an interface based on technology knowncurrently or to be known in the future may be provided in place of theinterface included in the visual page. For the sake of convenience, thefollowing description will be given on the assumption that the interfaceincluded in the visual page is provided.

FIG. 19 is an example of an interface 1900 generated based on theinterface information provided at step 1840.

Referring to FIG. 19, the interface 1900 includes a table, including acheck box column 1910, a reserved word candidate column 1920, and adetailed view column 1930. Furthermore, the interface 1900 may include areserved word addition button 1940, a candidate deletion button 1950,and an add-to-storage box button 1960. A user may select at least onedesired reserved word candidate on the check box column 1910, and mayprocess the reserved word candidate by selecting any one of the reservedword addition button 1940, the candidate deletion button 1950, and theadd-to-storage box button 1960. Once the any one of the buttons has beenselected, the terminal 200 may transfer input information, obtained byconverting the input of the user, to the information provision apparatus300.

The information provision apparatus 300 may process the reserved wordcandidate according to the input information received from the terminal200. For example, when a user selects the check boxes 1910 of somereserved word candidates (hereinafter referred to as the “selectedcandidates”) and also selects the reserved word addition button 1940,the control unit 320 of the information provision apparatus 300 havingrelated input information may add the selected candidates to a reservedword set and also delete the selected candidates from a reserved wordcandidate set. The control unit 320 performs control such that thelanguage units included in the reserved word set and linguistic unitssubstantially identical to the language units included in the reservedword set can be prevented from being recommended as reserved wordcandidates upon the future recommendation of reserved word candidates.

According to another example, when a user selects the check boxes 1910of some reserved word candidates and also selects the reserved wordcandidate deletion button 1950, the control unit 320 of the informationprovision apparatus 300 having received related input information mayadd the selected candidates to a reserved word exclusion set and deletethe selected candidates from the reserved word candidate set. Thecontrol unit 320 performs control such that the language units includedin the reserved word exclusion set and linguistic units substantiallyidentical to the language units included in the reserved word exclusionset can be prevented from being recommended as reserved word candidatesupon the future recommendation of reserved word candidates.

Furthermore, according to another example, when a user selects the checkboxes 1910 of some reserved word candidates and also selects theadd-to-storage box button 1960, the control unit 320 of the informationprovision apparatus 300 having received related input information mayadd the selected candidates to a reserved word candidate storage set anddelete the selected candidates from the reserved word candidate set. Thecontrol unit 320 performs control such that the language units includedin the reserved word candidate storage set and linguistic unitssubstantially identical to the language units included in the reservedword candidate storage set can be prevented from being recommended asreserved word candidates upon the future recommendation of reserved wordcandidates.

Another interface similar to the buttons or another interface capable ofreplacing the functions of the buttons may be used in place of thebuttons 1940, 1950 and 1960.

Furthermore, the control unit 320 may provide an interface adapted todelete part of reserved words from the reserved word set. The controlunit 320 may provide an interface adapted to delete a linguistic unitfrom the reserved word exclusion set such that part of the linguisticunits in the reserved word exclusion set are prevented from beingexcluded from recommendation. The control unit 320 may provide thelinguistic unit of the reserved word candidate storage set in the formof a list interface similar to that of FIG. 19, and may enable a user toadd part of the linguistic units of the reserved word candidate storageset as a reserved word via the list interface. Furthermore, a user mayinclude part of the linguistic units of the reserved word candidatestorage set in the reserved word exclusion set and delete the part ofthe linguistic units from the reserved word candidate storage set viathe list interface. In this case, the corresponding linguistic unit isnot provided via the list interface of the reserved word candidatestorage set any longer, and is not recommended as a reserved wordcandidate via the interface 1900 of FIG. 19 any longer. Furthermore, auser may simply delete part of the linguistic units of the reserved wordcandidate storage set from the reserved word candidate storage set viathe list interface. In this case, the corresponding linguistic unit isnot provided via the list interface of the reserved word candidatestorage set any longer, but may be recommended as a reserved wordcandidate via the interface 1900 of FIG. 19.

Furthermore, the interface 1900 may include a previous page button 1970and/or a subsequent page button 1980 for switching between pages inpreparation for a case where reserved word candidates cannot be alldisplayed within a single page. The previous page button 1970 and/or thesubsequent page button 1980 may be selectively provided depending on thenumber of actual candidates and a current page location. Furthermore,there may be provided an interface adapted to be extendable throughscrolling in place of the previous page button 1970 and/or thesubsequent page button 1980. In some interfaces, only a table includingthe items 1910, 1920 and 1930 may be scrolled, and the buttons 1940,1950, 1960, 1970 and 1980 may be excluded from scrolling.

A user may refer to a background for the recommendation of a reservedword candidate or related information in detail by selecting thedetailed view 1930. An interface which is provided by the control unit320 when the detailed view 1930 is selected may include an interfaceadapted to add information about a corresponding reserved word candidateand the corresponding reserved word candidate as a reserved word, to thereserved word candidate storage set, or to the reserved word exclusionset.

The control unit 320 may provide an interface configured to managereserved word candidates to a user via the terminal 200.

Referring back to FIG. 18, at step 1850, the control unit 320 may add aselected reserved word candidate to the reserved word set in response toan input adapted to select the reserved word.

Referring back to FIG. 13, at step 1330, the control unit 320 stores thedegree of object-keyword association corresponding to each objectitem-representative attribute keyword pair.

FIG. 10 shows an example of the stored degrees of object-keywordassociation according to an exemplary embodiment of the presentdisclosure.

In the exemplary embodiment of FIG. 10, object items are all in m (i₁ toi_(m)) in number, and representative attribute keywords are all n (k₁ tok_(n)) in number.

For example, the degree of object-keyword association between the objectitem i₅ and the representative attribute keyword k₃ is w_(5,3).

The process of step 1330 may be performed according to, e.g., part ofthe exemplary embodiments of FIGS. 5 to 8, or a process similar orcorresponding to the part. According to another exemplary embodiment,the process of step 1330 may be performed by the input of anadministrator or by receiving the degree of object-keyword association,determined by an external system, via a network or storage medium.

Since the exemplary embodiments of FIGS. 5 to 8 have been describedabove, redundant descriptions will be omitted. However, the process ofstep 510 of FIGS. 5 and 8 is substantially the same as the process ofstep 1310 of FIG. 13. Accordingly, when the process of step 1330 isperformed, the process of step 510 may not be performed again and theresult of step 1310 may be reused even when the exemplary embodiments ofFIGS. 5 to 8 are used.

At step 1340, the control unit 320 stores the degree of basic reservedword-keyword association corresponding to each pair of a reserved wordand a representative attribute keyword in the storage unit 330 by usingan association weight corresponding to a pair of the representativeattribute keyword and a subordinate keyword and the degree of basicreserved word-subordinate keyword association corresponding to a pair ofthe reserved word and the subordinate keyword. The process of step 1340may be performed according to, e.g., the input of an administrator orthe exemplary embodiments of any one or more of FIGS. 15 to 17.

Before or during the process of step 1340, the subordinate keyword needsto be determined, the association weight corresponding to the pair ofthe representative attribute keyword and the subordinate keyword needsto be determined, and the degree of basic reserved word-subordinatekeyword association corresponding to the pair of the reserved word andthe subordinate keyword needs to be determined.

The subordinate keyword used in the process of step 1340 may bedetermined through the performance of step 520 during the process ofstep 1330. In this case, the subordinate keyword of step 520 may be usedat step 1340. Unless the subordinate keyword is determined at step 1330,the subordinate keyword may be determined through step 520 of FIG. 5 anda process identical or similar to its previous process.

The association weight used in the process of step 1340 may bedetermined through the performance of step 530 during the process ofstep 1330. In this case, the association weight of step 530 may be usedat step 1340. Unless the association weight is determined at step 1330,the association weight may be determined step 530 of FIG. 5 and aprocess identical or similar to its previous process.

The degree of basic reserved word-subordinate keyword association may becalculated by, e.g., taking into account the frequency at which thereserved word and the subordinate keyword appear in the same context orsimilar contexts.

In the following, in the descriptions of FIGS. 15 to 17, an example ofobtaining the degree of basic reserved word-keyword association v_(3,2)between reserved word C₂ and representative attribute keyword k₃ isdescribed. For example, it is assumed that the subordinate keywords ofthe representative attribute keyword k₃ are B3₁ to B3₅₀. For thereserved word, the representative attribute keyword, and the degree ofbasic reserved word-keyword association, reference is made to theexample described above with reference to FIG. 11. The degree of basicreserved word-subordinate keyword association corresponding to a pair ofreserved word C_(j) and subordinate keyword B_(gh) is represented byx_(j,h). An association weight corresponding to a pair of thesubordinate keyword B_(gh) and representative attribute keyword k_(g) isrepresented by y_(g,h). The adjusted degree of reserved word-subordinatekeyword association corresponding to the combination of reserved wordC_(j), representative attribute keyword k_(g), and subordinate keywordB_(gh) is represented by x_(j,g,h).

FIG. 15 is a detailed flowchart of step 1340 according to a firstexemplary embodiment of the present disclosure.

Referring to FIG. 15, at step 1510, the control unit 320 acquires theadjusted degree of reserved word-subordinate keyword association byapplying the association weight to the degree of basic reservedword-subordinate keyword association.

At step 1510, for each reserved word-subordinate keyword pair, thecontrol unit 320 may obtain the adjusted degree of reservedword-subordinate keyword association corresponding to the reservedword-subordinate keyword pair for the representative attribute keywordby applying an association weight corresponding to a subordinatekeyword-representative attribute keyword pair to the degree of basicreserved word-subordinate keyword association corresponding to thereserved word-subordinate keyword pair.

For example, in order to acquire the adjusted degree of reservedword-subordinate keyword association between C₂ and B3₄ corresponding toa pair of subordinate keyword B3₄ and representative attribute keywordk₃ when a reserved word is C₂, the control unit 320 may obtain theadjusted degree of reserved word-subordinate keyword associationx_(2,3, 4) by applying association weight y_(3,4) corresponding to thepair of the subordinate keyword B3₄ and the representative attributekeyword k₃ to the degree of basic reserved word-subordinate keywordassociation x_(2,4) corresponding to a pair of the reserved word C₂ andthe subordinate keyword B3₄.

In particular, a method of applying an association weight may be amethod of multiplying the degree of basic reserved word-subordinatekeyword association x_(2,4) by the association weight y_(3,4)corresponding to the pair of the subordinate keyword B3₄ and therepresentative attribute keyword k₃. For example, in order to acquirethe adjusted degree of reserved word-subordinate keyword associationx_(2,3,4) between C₂ and B3₄ corresponding to a pair of the subordinatekeyword B3₄ and the representative attribute keyword k₃ when thereserved word is C₂, the control unit 320 may acquire the adjusteddegree of reserved word-subordinate keyword association x_(2,3,4) byusing value x_(2,4)×y_(3,4) obtained by multiplying the pair of thereserved word C₂ and the subordinate keyword B3₄ corresponding to thedegree of basic reserved word-subordinate keyword association x_(2,4) byassociation weight y_(3,4) corresponding to the pair of the subordinatekeyword B3₄ and the representative attribute keyword k₃. In anotherexemplary embodiment, the control unit 320 may acquire the adjusteddegree of reserved word-subordinate keyword association x_(2,3,4) byusing function f(x_(2,4),y_(3,4)) based on anothercalculation/utilization method adapted to allow the adjusted degree ofreserved word-subordinate keyword association x_(2,3,4) to have apositive correlation with x_(2,4) and y_(3,4) in place of themultiplication. Furthermore, both a method of using (x_(2,4)×v_(3,4)) asthe adjusted degree of reserved word-subordinate keyword associationx_(2,3,4) and a method of applying another factor-based correction to(x_(2,4)×v_(3,4)) and then using a resulting value as the adjusteddegree of reserved word-subordinate keyword association x_(2,3,4) may beused.

At step 1520, the control unit 320 may set the degree of basic reservedword-keyword association by using the cumulative value of the adjusteddegrees of reserved word-subordinate keyword association x_(2,3,f)between the reserved word C₂ and the representative attribute keywordk₃. In other words, the degree of basic reserved word-keywordassociation between the reserved word C₂ and the representativeattribute keyword k₃ may be Σ_(f=1) ⁵⁰x_(2,3,f). In other words, thedegree of basic reserved word-keyword association between the reservedword C₂ and the representative attribute keyword k₃ may be obtained byobtaining the degrees of basic reserved word-subordinate keywordassociation x_(2,f) with reserved word C₂ for subordinate keywords B3₁to B3₅₀, obtaining x_(2,3,f) by incorporating an association weighty_(3,f) for the corresponding subordinate keyword to each of the degreesof basic reserved word-subordinate keyword association x_(2,f), and thenaccumulating x_(2,3,f). According to another exemplary embodiment, thedegree of basic reserved word-keyword association between the reservedword C₂ and the representative attribute keyword k₃ may be a valueobtained by applying another factor-based correction to Σ_(f=1)⁵⁰x_(2,3,f). According to another exemplary embodiment, the degree ofbasic reserved word-keyword association between the reserved word C₂ andthe representative attribute keyword k₃ may be a value having a positivecorrelation with Σ_(f=1) ⁵⁰x_(2,3,f). In this case, it is assumed thatsubordinate keywords connected to one representative attribute keywordare 50 in number. However, when the number of subordinate keywordsconnected to a representative attribute keyword varies, the cumulativerange of f may become a different value, other than 50, in the formula.

FIG. 16 is a detailed flowchart of step 1340 according to anotherexemplary embodiment of the present disclosure. Since the processes ofsteps 1510 and 1520 of FIG. 16 are the same as the processes describedwith reference to FIG. 15, redundant descriptions will be omitted.

Referring to FIG. 16, at step 1530, the control unit 320 may delete thedegree of basic reserved word-keyword association for at least onerepresentative keyword, for which the degree of basic reservedword-keyword association for a pair of the reserved word and thecorresponding representative keyword is equal or lower than thereference degree of basic reserved word-keyword association, amongrepresentative keywords corresponding to a specific reserved word.Representative keywords corresponding to a specific reserved word referto keywords for which the degrees of basic reserved word-keywordassociation have been set in the relationship with the specific reservedword. The reference degree of basic reserved word-keyword associationmay be set in advance. According to another exemplary embodiment, thereference degree of basic reserved word-keyword association may be setusing the average value of the degrees of basic reserved word-keywordassociation corresponding to the specific reserved word, or may be setusing the degree of basic reserved word-keyword association at aspecific order position when the degrees of basic reserved word-keywordassociation corresponding to the specific reserved word are arranged inthe order of their size. Another specific value having a positivecorrelation with the degrees of basic reserved word-keyword associationcorresponding to the specific reserved word may become the referencedegree of basic reserved word-keyword association. The reference degreeof basic reserved word-keyword association may vary depending on areserved word, and may be the same for all reserved words. The deletionof the degree of basic reserved word-keyword association means thatthere is no degree of association between a reserved word and arepresentative keyword. The control unit 320 may set the degree ofassociation to 0. Alternatively, the control unit 320 may delete thedegree of basic reserved word-keyword association in such a manner as todelete information about the degree of basic reserved word-keywordassociation of a pair of the reserved word and the correspondingrepresentative keyword in a list (which may be replaced with an array,or another data structure) showing the degrees of basic reservedword-keyword association. There may be used another method of addinginformation indicating that the degree of basic reserved word-keywordassociation has been deleted (or an associative relationship has beendeleted).

Through the process of step 1530, the degree of basic reservedword-keyword association having a relatively slight degree ofassociation is deleted (i.e., a setting is made such that there is nodegree of association), and thus excessively complicated calculation maybe prevented from being performed or an associative relationshipsubstantially meaningless to a user/administrator may be prevented frombeing displayed.

FIG. 17 is a detailed flowchart of step 1340 according to still anotherexemplary embodiment of the present disclosure.

Since the processes of steps 1510 and 1520 of FIG. 17 are the same asthe processes described with reference to FIG. 15, redundantdescriptions will be omitted. Furthermore, since the process of step1530 of FIG. 17 is the same as the processes described with reference toFIG. 16, a redundant description will be omitted.

At step 1540, the control unit 320 may normalize the degrees of basicreserved word-keyword association which have not been deleted andremain. For example, in order to include the average value of thedegrees of basic reserved word-keyword association, stored in connectionwith a specific representative attribute keyword, in a specific range,the degrees of basic reserved word-keyword association stored inconnection with the specific representative attribute keyword may beincreased or decreased by multiply the degrees of basic reservedword-keyword association by a predetermined coefficient. For example, inorder to include the total sum of the degrees of basic reservedword-keyword association stored in connection with a specificrepresentative attribute keyword in a specific range, the degrees ofbasic reserved word-keyword association stored in connection with thespecific representative attribute keyword may be increased or decreasedby multiplying the degrees of basic reserved word-keyword association bya predetermined coefficient. In other words, appropriate adjustment maybe performed to prevent a case where only a specific representativeattribute keyword is recommended/used or the specific representativeattribute keyword is rarely used even when any reserved word is selectedbecause the degree of basic reserved word-keyword association having ahigh value is concentrated on the specific representative attributekeyword.

According to another exemplary embodiment, at step 1540, the controlunit 320 may perform normalizing by adding a predetermined coefficientto the degrees of basic reserved word-keyword association or applying acombination with an arithmetic operation, such as a log operation, anexponential operation, or the like in place of multiplying them by thepredetermined coefficient. According to still another exemplaryembodiment, the control unit 320 may perform normalizing in such amanner as to decrease only the degrees of basic reserved word-keywordassociation equal to or higher than a specific reference value orincrease only the degrees of basic reserved word-keyword associationequal to or lower than a specific reference value.

Furthermore, according to another exemplary embodiment, at step 1540,the control unit 320 may perform normalization in order to include theaverage value (or the total sum) of the degrees of basic reservedword-keyword association stored in connection with a specific reservedword in a specific range.

There may be possible a modified exemplary embodiment in which step 1530is omitted in the process of FIG. 17 and the normalization of thedegrees of basic reserved word-keyword association is performed.

At step 1350, the communication unit 310 acquires a received reservedword by receiving the received reserved word from the terminal 200, andtransfers the received reserved word to the control unit 320.

The received reserved word is a reserved word which is received by theterminal 200 from a search user. The terminal 200 may convert a voiceinput into an electrical signal (a voice signal), and may transferinformation to the provision device 300. The control unit 320 of theinformation provision apparatus 300 may convert the voice signal into atext by analyzing the voice signal, and may match the resulting text toa reserved word. Furthermore, the control unit 320 may analyze theintonation, pitch, tempo, respiration state, etc. of a voice byanalyzing the voice signal, and may use analysis results as contextualinformation.

According to another exemplary embodiment, the terminal 200 may convertthe voice input into a text, and may transfer the text to theinformation provision apparatus 300. The terminal 200 may analyze theintonation, pitch, tempo, respiration state of a voice, etc., and maytransfer analysis information to the information provision apparatus300. The information provision apparatus 300 may use the analysisinformation as a type of contextual information.

At step 1360, the control unit 320 acquires the degree of reservedword-object association corresponding to a pair of the received reservedword and each object item by using the degree of object-keywordassociation and the degree of basic reserved word-keyword association.The process of step 1360 may be performed according to the method ofstep 940 of FIG. 9 or the method of FIG. 12. The same descriptions willbe omitted.

At step 1370, the control unit 320 may provide an object item accordingto the degree of reserved word-object association corresponding to thereceived reserved word. The process of step 1370 may be performed in thesame manner as the process of step 950. The same descriptions will beomitted.

FIG. 14 is a flowchart of a process of providing information accordingto still another exemplary embodiment of the present disclosure.

Since steps 1310, 1320, 1330, 1340, 1350, 1360 and 1370 of FIG. 14 arethe same as steps 1310, 1320, 1330, 1340, 1350, 1360 and 1370 of FIG.13, the same description will not be repeated.

Step 1333 added to FIG. 14 may be performed at any time after arepresentative attribute keyword and a subordinate keyword have beendetermined. For example, step 1333 may be performed at the same timeas/in parallel with step 1330, or may be performed during step 1330.

At step 1333, the control unit 320 stores an association weight betweenthe representative attribute keyword and the subordinate keyword. In thecase where the process of setting an association weight is not performedat step 1330, an association weight between the representative attributekeyword and the subordinate keyword may be set through a processidentical or similar to the process of step 530 of FIG. 5. According toanother exemplary embodiment, the control unit 320 may store anassociation weight between the representative attribute keyword and thesubordinate keyword in such a manner as to retrieve the associationweight set at step 1330.

At step 1337, the control unit 320 acquires the degree of basic reservedword-subordinate keyword association between the subordinate keyword andthe reserved word.

The control unit 320 may determine the degree of association between thereserved word and the subordinate keyword, e.g., by taking into accountthe frequency at which the subordinate keyword appears in a contextidentical or similar to a context in which the reserved word appears.For example, words appearing in the vicinity of keyword A in a specificsentence may be viewed as appearing in the vicinity of words associatedwith the keyword A in other documents.

“I went on a trip after making a hard decision, but it was July and,thus, the weather was so hot that I suffered.”

“I went on a trip after making a hard decision, but it was July and,thus, the weather was so humid that I suffered.”

Referring to the above two sentences, the word “hot” is replaced withthe word “humid” in the same context. The control unit 320 may inferthat “hot” and “humid” are associated words.

“I went on a trip after making a hard decision, but it was July and,thus, the weather was so hot that I suffered.”

“I went on vacation after making a hard decision, but it was July and,thus, the weather was so hot that I suffered.”

In the same manner, the control unit 320 may infer from the above twosentences that “trip” and “vacation” are associated words.

“I went on a trip after making a hard decision, but it was July and,thus, the weather was so hot that I suffered.”

“I went on a trip after making a hard decision, but it was August and,thus, the weather was so hot that I suffered.”

In the same manner, the control unit 320 may infer that “July” and“August” are associated words.

The control unit 320 may stores information in which “hot” and “humid”are associated words, “July” and “August” are associated words, and“trip” and “vacation” are associated words via previously collecteddocuments. Thereafter, it is assumed that the following sentences arecollected.

“I went on vacation after making a hard decision, but it was July and,thus, the weather was so hot that I suffered.”

“I went on a trip after making a hard decision, but it was August and,thus, the weather was so hot that I went through hardship.”

When the two sentences do not have the same context but it is known that“hot” and “humid” are associated words, “July” and “August” areassociated words, and “trip” and “vacation” are associated words, thecontrol unit 320 may learn via the above sentences that “suffer” and“hardship” are also associated words.

It may be determined that a keyword pair having a high appearingfrequency in the same/similar contexts has a high degree of association.Furthermore, it is determined that the higher the similarity betweencontexts in which two keywords appear is, the higher the degree ofassociation between the two keywords is. The control unit 320 mayincrease the accuracy of the determination of the degrees of associationbetween keywords in such a manner as to set the degrees of associationkeywords by performing learning by using collected documents and thensetting the degrees of association between keywords appearing in acorresponding sentence by using the set degrees of association betweenkeywords and the context of the sentence.

As similar learning methods, Neural Net Language Model (NNLM), RecurrentNeural Net Language Model (RNNLM), word2vec, skipgram, and ContinuousBag-of-Words (CBOW) methods are known. In particular, when the word2vecmethod is used, the word2vec method may map individual keywords tovectors by performing learning by using documents, and may determine thesimilarity between two keywords through the cosine similaritycomputation of two vectors.

FIG. 20 is a view of a terminology hierarchy according to an exemplaryembodiment of the present disclosure.

Once the process of FIG. 13 or 14 has been completed, there are set thehierarchical relationship between reserved words C₁ to C_(q),representative attribute keywords k₁ to k_(n), and subordinate keywordsBX₁ to BX₅₀.

The degrees of basic reserved word-keyword association are set betweenthe reserved words and the representative attribute keywords, andassociation weights are set between the representative attributekeywords and the subordinate keywords. Using this hierarchicalrelationship, the control unit 320 may perform the operation ofrecommending an appropriate object according to a reserved word, theoperation of selecting a new reserved word candidate, or the like.Furthermore, the hierarchical relationship of FIG. 20 may be amended orimproved by learning through the repetition of the process of FIG. 13 or14, with new data being incorporated into the hierarchical relationship.

According to at least some exemplary embodiments of the presentdisclosure, there are provided a method and apparatus for efficientlyproviding information by using a reserved word.

In this case, it can be understood that individual blocks of theflowcharts and/or combinations of the blocks of the flowcharts may beperformed by computer program instructions. Since it is possible toinstall these computer program instructions on a general-purposecomputer, a special computer, or the processor of a programmable dataprocessing device, the instructions executed through the computer or theprocessor of the programmable data processing device generate a meansfor performing functions which are described in the blocks of theflowcharts. Furthermore, since it is possible to store these computerprogram instructions in computer-usable or computer-readable memory thatcan be oriented to a computer or some other programmable data processingdevice in order to implement functions in a specific manner, it ispossible to manufacture products in which instructions stored incomputer-usable or computer-readable memory include means for performingfunctions described in the blocks of flowcharts. Moreover, since it ispossible to install computer program instructions on a computer oranother programmable data processing device, instructions for performinga series of operational steps on the computer or the programmable dataprocessing device, generating processes executed by the computer andoperating the computer or the programmable data processing device canprovide steps for performing functions described in the blocks offlowcharts.

Furthermore, each block may refer to part of a module, a segment, orcode including one or more executable instructions for performing one ormore specific logical functions. Moreover, it should be noted that insome alternative embodiments, functions described in blocks may occurout of order. For example, two successive blocks may be actuallyperformed at the same time, or sometimes may be performed in reverseorder according to relevant functions.

In this case, the term “unit” used herein refers to a software orhardware component, such as an FPGA or ASIC, which performs a function.However, the term “unit” is not limited to a software or hardwarecomponent. The unit may be configured to be stored in an addressablestorage medium, or may be configure to run one or more processors. Forexample, the unit may include components, such as software components,object-oriented software components, class components and taskcomponents, processes, functions, attributes, procedures, subroutines,segments of program codes, drivers, firmware, microcode, circuits, data,databases, data structures, tables, arrays, and variables. Functionsprovided by components and units may be combined into a smaller numberof components and units, or may be divided into a larger number ofcomponents and units. Furthermore, components and units may be eachimplemented to run one or more CPUs within a device or securitymultimedia card.

It will be understood by those having ordinary knowledge in the art towhich the present disclosure pertains that the present disclosure may bepracticed in other specific forms without changing the technical spiritor essential feature of the present disclosure. Therefore, theabove-described embodiments should be understood as being illustrative,not limitative, in all aspects. The scope of the present disclosure isdefined based on the attached claims rather than the detaileddescription, and the claims, equivalents to the claims, and allmodifications and alterations derived from the claims and theequivalents should be construed as being included in the scope of thepresent disclosure.

Meanwhile, although the exemplary embodiments of the present disclosurehave been disclosed in the present specification and the accompanyingdrawings and the specific terms have been used, this is intended merelyto easily describe the technical spirit of the present disclosure andhelp to understand the present disclosure, but is not intended to limitthe scope of the present disclosure. It will be apparent to those havingordinary knowledge in the art to which the present invention pertainsthat other modified exemplary embodiments based on the technical spiritof the present invention may be implemented in addition to the disclosedexemplary embodiments.

What is claimed is:
 1. A method of providing information, the methodcomprising: extracting a representative attribute keyword candidate setincluding representative attribute keywords; setting a reserved word setincluding reserved words; storing degrees of object-keyword associationcorresponding to object item-representative attribute keyword pairs;storing degrees of basic reserved word-keyword association correspondingto reserved word-representative attribute keyword pairs by usingassociation weights corresponding to representative attributekeyword-subordinate keyword pairs and degrees of basic reservedword-subordinate keyword association corresponding to reservedword-subordinate keyword pairs; acquiring a received reserved word;acquiring a degree of reserved word-object association corresponding toa pair of the received reserved word and each object item by using thedegrees of object-keyword association and the degrees of basic reservedword-keyword association; and providing an object item based on thedegree of reserved word-object association corresponding to the pair ofthe received reserved word and each object item.
 2. The method of claim1, further comprising, before storing the degrees of basic reservedword-keyword association: storing the association weights correspondingto the representative attribute keyword-subordinate keyword pairs; andacquiring degrees of basic reserved word-subordinate keyword associationbetween each subordinate keyword corresponding to the representativeattribute keywords and the reserved words.
 3. The method of claim 2,wherein acquiring the degrees of basic reserved word-subordinate keywordassociation comprises: determining the degrees of basic reservedword-subordinate keyword association between each subordinate keywordand the reserved words by taking into account a frequency at which eachsubordinate keyword appears in a context identical or similar to acontext in which the reserved words appear.
 4. The method of claim 1,wherein storing the degrees of basic reserved word-keyword associationcomprises: acquiring adjusted degrees of reserved word-subordinatekeyword association corresponding to the reserved word-subordinatekeyword pairs for the representative attribute keywords by applying theassociation weights corresponding to the representative attributekeyword-subordinate keyword pairs to the degrees of basic reservedword-subordinate keyword association corresponding to the reservedword-subordinate keyword pairs for each reserved word-subordinatekeyword pair; and setting degrees of basic reserved word-keywordassociation corresponding to pairs of the reserved words and a specificrepresentative attribute keyword so that the degrees of basic reservedword-keyword association pair have a positive correlation with acumulative value of the adjusted degrees of reserved word-subordinatekeyword association for the specific representative attribute keyword.5. The method of claim 4, wherein acquiring the adjusted degrees ofreserved word-subordinate keyword association comprises: acquiring theadjusted degree of reserved word-subordinate keyword associationcorresponding to the reserved word-subordinate keyword pairs for therepresentative attribute keywords by using a value obtained bymultiplying the degrees of basic reserved word-subordinate keywordassociation corresponding to the reserved word-subordinate keyword pairsfor each reserved word-subordinate keyword pair by the associationweights corresponding to the representative attributekeyword-subordinate keyword pairs.
 6. The method of claim 4, whereinstoring the degrees of basic reserved word-keyword association furthercomprises: deleting a degree of basic reserved word-keyword associationfor a representative keyword, for which a degree of basic reservedword-keyword association of a pair of a specific reserved word and acorresponding representative keyword is equal to or lower than thereference degree of basic reserved word-keyword association, amongrepresentative keywords having degrees of basic reserved word-keywordassociation corresponding to the specific reserved word.
 7. The methodof claim 4, wherein storing the degrees of basic reserved word-keywordassociation further comprises: deleting degrees of basic reservedword-keyword association for representative keywords exclusive of apredetermined number of representative keywords, selected in descendingorder of degrees of basic reserved word-keyword association of pairs ofa specific reserved word and the corresponding representative keywords,from representative keywords corresponding to the specific reservedword.
 8. The method of claim 6 or 7, wherein storing the degrees ofbasic reserved word-keyword association further comprises: normalizingnot deleted ones of the degrees of basic reserved word-keywordassociation corresponding to the specific reserved word so as tocomplement the degrees of basic reserved word-keyword association. 9.The method of claim 1, wherein setting the reserved word set comprises:setting a reserved word candidate by taking into account a frequency atwhich one linguistic unit or two or more consecutive linguistic units indocuments included in a document set and a frequency at which anemotional word included in an emotional word dictionary is locatedwithin a preset distance from the one linguistic unit or the two or moreconsecutive linguistic units; providing interface information adapted togenerate a reserved word selection interface including information aboutthe set reserved word candidate; and when a reserved word selectioninput is received via the reserved word selection interface, adding thereserved word candidate, selected according to the selection input, tothe reserved word set.
 10. An apparatus for providing information, theapparatus comprising: a control unit configured to extract arepresentative attribute keyword candidate set including representativeattribute keywords, to set a reserved word set including reserved words,store degrees of object-keyword association corresponding to objectitem-representative attribute keyword pairs, and to store degrees ofbasic reserved word-keyword association corresponding to reservedword-representative attribute keyword pairs by using association weightscorresponding to representative attribute keyword-subordinate keywordpairs and degrees of basic reserved word-subordinate keyword associationcorresponding to reserved word-subordinate keyword pairs; a storage unitconfigured to store the degrees of object-keyword association and thedegrees of basic reserved word-keyword association; and a communicationunit configured to acquire a received reserved word; wherein the controlunit acquires a degree of reserved word-object association correspondingto a pair of the received reserved word and each object item by usingthe degrees of object-keyword association and the degrees of basicreserved word-keyword association; and wherein the control unit providesan object item based on the degree of reserved word-object associationcorresponding to the pair of the received reserved word and each objectitem.
 11. The apparatus of claim 10, wherein the control unit, beforestoring the degrees of basic reserved word-keyword association in thestorage unit: stores the association weights corresponding to therepresentative attribute keyword-subordinate keyword pairs in thestorage unit; and acquires degrees of basic reserved word-subordinatekeyword association between each subordinate keyword corresponding tothe representative attribute keywords and the reserved words.
 12. Theapparatus of claim 11, wherein the control unit: determines the degreesof basic reserved word-subordinate keyword association between eachsubordinate keyword and the reserved words by taking into account afrequency at which each subordinate keyword appears in a contextidentical or similar to a context in which the reserved words appear.13. The apparatus of claim 10, wherein the control unit: acquiresadjusted degrees of reserved word-subordinate keyword associationcorresponding to the reserved word-subordinate keyword pairs for therepresentative attribute keywords by applying the association weightscorresponding to the representative attribute keyword-subordinatekeyword pairs to the degrees of basic reserved word-subordinate keywordassociation corresponding to the reserved word-subordinate keyword pairsfor each reserved word-subordinate keyword pair; and sets degrees ofbasic reserved word-keyword association corresponding to pairs of thereserved words and a specific representative attribute keyword so thatthe degrees of basic reserved word-keyword association pair have apositive correlation with a cumulative value of the adjusted degrees ofreserved word-subordinate keyword association for the specificrepresentative attribute keyword.
 14. The apparatus of claim 13, whereinthe control unit: acquires the adjusted degree of reservedword-subordinate keyword association corresponding to the reservedword-subordinate keyword pairs for the representative attribute keywordsby using a value obtained by multiplying the degrees of basic reservedword-subordinate keyword association corresponding to the reservedword-subordinate keyword pairs for each reserved word-subordinatekeyword pair by the association weights corresponding to therepresentative attribute keyword-subordinate keyword pairs.
 15. Theapparatus of claim 13, wherein the control unit: deletes a degree ofbasic reserved word-keyword association for a representative keyword,for which a degree of basic reserved word-keyword association of a pairof a specific reserved word and the corresponding representative keywordis equal to or lower than a reference degree of basic reservedword-keyword association, among representative keywords having degreesof basic reserved word-keyword association corresponding to the specificreserved word.
 16. The apparatus of claim 13, wherein the control unit:deletes degrees of basic reserved word-keyword association forrepresentative keywords exclusive of a predetermined number ofrepresentative keywords, selected in descending order of degrees ofbasic reserved word-keyword association of pairs of a specific reservedword and the corresponding representative keywords, from representativekeywords corresponding to the specific reserved word.
 17. The apparatusof claim 15 or 16, wherein the control unit: normalizes not deleted onesof the degrees of basic reserved word-keyword association correspondingto the specific reserved word so as to complement the degrees of basicreserved word-keyword association.
 18. The apparatus of claim 10,wherein: the control unit sets a reserved word candidate by taking intoaccount a frequency at which one linguistic unit or two or moreconsecutive linguistic units appear in documents included in a documentset and a frequency at which an emotional word included in an emotionalword dictionary is located within a preset distance from the onelinguistic unit or the two or more consecutive linguistic units; thecommunication unit provides interface information adapted to generate areserved word selection interface including information about the setreserved word candidate; and when the communication unit receives areserved word selection input via the reserved word selection interface,the control unit adds the reserved word candidate, selected according tothe selection input, to the reserved word set.